Page 136 - JOURNAL OF LIBRARY SCIENCE IN CHINA 2018 Vol. 43

136 Journal of Library Science in China, Vol.9, 2017

covers the following aspects. First, the data can be expected to solve the problem of the absence
of a unified and normalized data source in research on and applications of academic literature
usage. Notwithstanding the competition from Scopus, Google Scholar and other platforms,
as far as the recognition degree of data as a key evaluation element is concerned, the current
application status of WoS is still appreciable. In constructing the “double-first-class” of colleges
and universities recently launched by China, the quantitative criterion of current actual operations
in “the construction of first-class disciplines” is the Essential Science Indicators (ESI) based on the
databases SCI and SSCI of the WoS platform. Second, the usage data of the WoS platform not only
cover download data, but also cover the export (usage) count of literature title information, which
is the new data adaptive to the current reform of academic reading habits. With the rapid growth
in the quantity of academic literature, the fast reading of bibliographic data by academic users will
become a new norm and the data will have a measurement value. Third, constructed on the basis
of the grassroots academic entities of single academic articles, the usage data can be expanded
upwards to realize the analysis on more meso and macro-level academic objects, such as scholars,
journals, institutions, research topics, countries and regions. It is clear that the emergence of the
new data can be expected to drive systematic research on and applications of the magnitude data
for academic literature.
The usage data of the WoS platform represent records on the database platform, which have
a boundary in terms of data range or user scope. This makes it possible to engage in theoretical
constructions.
Proposition 1: There is an approximately linear relationship between the academic literature
usage value,U, of the Web of Science and the actual total usage count,N, of academic literature.
Proof: For a specific discipline, the total usage count of academic literature is set as N, and the
ratio of the WoS platform-based usage behaviors in all usage behaviors is α. In the use of WoS, the
download ratio is β, and the export ratio is γ, then the usage value,U, in WoS can be expressed as
U=α·β·N+α·γ·N=α·(β+γ)·N. At a certain moment of measurement, α is a constant value
whilst β and γ are user behavior parameters with approximate stability. So, by settingα·(β+γ) as
constant δ (WoS usage factor), we have approximately U=δ·N.
Proposition 1 suggests that the usage data of the WoS platform constitutes a subset of all usage
data and that their share is relatively stable. Meanwhile, WoS has its entry conditions and its users
mostly come from colleges, universities, scientific research institutions, enterprises and public
institutions with a certain strength. These users constitute the relatively high-level portion of
scientific and technological knowledge users, so we have Inference 1-1.
Deduction 1-1: The academic literature usage data of the Web of Science represent an intensive
sampling from the actual total usage count of academic literature.
Proposition 2: There is a positive relationship trend between the academic literature usage
value, U, of the Web of Science and the citation value, C, under a large sample volume.
Proof: For a specific discipline, the total usage count of academic literature is set as N, the